1. 1-1 TITLE PRESENTATION:HEALTHCARE GROUP MEMBER: CHUAH XUE LI(212176) ONG SEAT NEE(212133) STIN2063 MACHINE LEARNING

2. Introduction Healthcare is the medical field in which various services are provided to the public. Healthcare consists of caring for the human body by treating illnesses and performing other medical services. The title of the journal paper is Predicting Breast Screening Attendance. Copyright 2010 John Wiley & Sons, Inc. 2

3. Introduction Breast cancer is a disease in which malignant (cancer) cells form in the tissues of the breast. Breast cancer is the second leading cause of cancer deaths in women today (after lung cancer) and is the most common cancer among women, except for skin cancers. Copyright 2010 John Wiley & Sons, Inc. 3

4. What Is Knowledge Discovery  Defines as the fields of discovering potentially useful information from large amount of data in the context of healthcare application. Copyright 2010 John Wiley & Sons, Inc. 4

5. What is Data Mining  Defines as analytical tools that can be used to extract meaningful knowledge from large data set in the context of healthcare application. Copyright 2010 John Wiley & Sons, Inc. 5

6. What is Machine Learning  Defines as providing computational method for accumulating, changing and updating knowledge in intelligent system,  And in particular learning mechanisms that will help us to induce knowledge from example or data in the context of healthcare application. Copyright 2010 John Wiley & Sons, Inc. 6

7. Problem  Breast cancer is the one of the all cancer highest in the world.  According to statistics, there are over 40000 women are being diagnosed with breast cancer each year in U.K. Copyright 2010 John Wiley & Sons, Inc. 7

8. Objective  To predict breast screening attendance using machine learning. Copyright 2010 John Wiley & Sons, Inc. 8

9. Data Mining Function Prediction Copyright 2010 John Wiley & Sons, Inc. 9

10. Data Mining Function The data preprocessing module identified episodes with missing data and removed them from the study. In total 2% (9 799) were removed as records with missing data (see Table I). It further deleted almost 3% (15 778) of the total records due to duplicate entries. The valid records constituted 86% (159 412) of the extracted dataset; on an average, each record had 3.2 episodes. Copyright 2010 John Wiley & Sons, Inc. 10

11. Data Mining Function-Prediction Copyright 2010 John Wiley & Sons, Inc. 11

12. Machine Learning Method  Evolutionary Learning (Genetic Algorithm) Copyright 2010 John Wiley & Sons, Inc. 12

13. Conclusion  Machine learning can help to diagnosed the breast cancer at the earlier stages.  Patient can take the action to avoid the spread of the affected cells.  Thus, it can decreases the percentage of death due to breast cancer. Copyright 2010 John Wiley & Sons, Inc. 13

14. Copyright 2010 John Wiley & Sons, Inc. 14